Following the transformer architecture [1]}, the global encoder layer makes use of two sub-layers, namely, a multi-head self-attention mechanism for enabling further integration of information and a fully connected feed-forward network for deep feature extraction. Residual connection and layer normalization are employed around each of these two sub-layers.
